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Smart Agriculture ›› 2026, Vol. 8 ›› Issue (4): 164-179.doi: 10.12133/j.smartag.SA202512028

• 信息处理与决策 • 上一篇    

基于PLUS-InVEST耦合模型的平原农区土地利用多情景模拟与碳储量时空演变研究

王高程1, 刘健1, 李莎莎1, 张婷婷1, 王瑷玲1,2()   

  1. 1. 山东农业大学资源与环境学院,山东 泰安 271018,中国
    2. 土肥高效利用国家工程研究中心,山东 泰安 271018,中国
  • 收稿日期:2025-12-30 出版日期:2026-07-30
  • 基金项目:
    山东省自然科学基金(ZR2021MD018;ZR2025QC396)
  • 作者简介:

    王高程,硕士研究生,研究方向为土地利用与信息技术。E-mail:

  • 通信作者:
    王瑷玲,博士,教授,研究方向为土地利用与信息技术。E-mail:

Multi-Scenario Simulation of Land Use and Spatiotemporal Evolution of Carbon Storage in Plain Agricultural Region Based on The Coupled PLUS-InVEST Model

WANG Gaocheng1, LIU Jian1, LI Shasha1, ZHANG Tingting1, WANG Ailing1,2()   

  1. 1. Shandong Agricultural University, College of Resources and Environment, Taian 271018, China
    2. National Engineering Research Center for Efficient Utilization of Soil and Fertilizer Resources, Taian 271018, China
  • Received:2025-12-30 Online:2026-07-30
  • Foundation items:Natural Science Foundation of Shandong Province(ZR2021MD018;ZR2025QC396)
  • About author:

    WANG Gaocheng, E-mail:

  • Corresponding author:
    WANG Ailing, E-mail:

摘要:

【目的/意义】 碳储量是衡量生态系统功能的重要指标,不同土地类型的固碳能力不同,分析并模拟土地利用变化与碳储量时空特征,可为土地利用优化和区域碳损失风险评估提供依据。 【方法】 以典型平原农业县山东省高唐县为研究区,运用斑块生成土地利用模拟模型与生态系统服务与权衡综合评估模型(Patch-generating Land Use Simulation-Integrated Valuation of Ecosystem Services and Trade-offs, PLUS-InVEST),模拟自然发展、耕地保护、城镇发展、生态保护和可持续发展5种情景下土地利用变化,估算不同情景下碳储量特征。 【结果和讨论】 2009—2023年地类变化明显,尤其是林地与耕地之间转换最显著;期间耕地面积净减少3 184.10 hm2,林地面积净增加1 988.74 hm2,2009—2023年碳储量总体增加1.14×105 t,2014-2019年总体增幅约3.56%,耕地碳储量减少最多,林地增加最多;2035年各情景下土地利用类型的空间分布格局基本一致,均呈现中心低、四周高的空间分布规律。不同情景下碳储量均有所不同,城镇发展情景下总碳储量最低,相较于2023年减少3.01×105 t。 【结论】 应注重耕地保护、生态保护与城镇建设的协同发展,避免单一情景政策的局限性。研究通过PLUS-InVEST耦合模型有效揭示了土地利用变化对碳储量时空分布的影响,探明了不同情景下碳储量空间分布特征,为“双碳”和可持续发展目标的实现提供借鉴。

关键词: 土地利用, 碳储量, PLUS-InVEST耦合模型, 多情景模拟, 时空演变, 平原农区

Abstract:

[Objective] Carbon storage is a key indicator for measuring ecosystem functionality, with significant differences in carbon sequestration capacity among various land use types. Changes in land use directly lead to variations in terrestrial ecosystem carbon storage. Therefore, an in-depth analysis and prediction of the spatiotemporal distribution patterns of land use change and carbon storage can provide a scientific basis for achieving carbon sequestration targets and optimizing land use structures. [Methods] Taking Gaotang county, a typical plain agricultural county in Shandong province as the study area, based on land use types extracted from remote sensing data from 2009 to 2023, the spatiotemporal evolution patterns of land use were first analyzed. The InVEST model was then employed to estimate carbon storage and identify the spatial distribution patterns of carbon storage across different land categories. Driving factors were selected from the perspectives of natural conditions, socio-economic development, and locational conditions. Five scenarios: natural development, cultivated land protection, urban development, ecological protection, and sustainable development, were established. A coupled PLUS-InVEST framework was constructed. First, the PLUS model was used to simulate land use patterns under five scenarios for 2035. Then, the simulated land use maps were input into the InVEST model to estimate carbon storage and to compare changes across different scenarios. This coupling enables an integrated analysis linking policy scenarios, spatial land use patterns, and carbon storage responses. [Results and Discussions] From 2009 to 2023, significant changes occurred in land use types, with the most notable transformation being the conversion from cultivated land to forest land. Overall, cultivated land area fluctuated and decreased by 3 184.10 hm2; forest land in the southwestern region increased by 1 988.74 hm2; and construction land in the county center decreased by 109.89 hm2. Total carbon storage increased by 1.14×105 t. Carbon storage was significantly correlated with the spatial distribution of various land types, exhibiting a pattern of higher values in the southwest and lower values in the northeast. Carbon storage in the county center was relatively low, while the southwestern region, rich in forest land resources, had the highest carbon storage. Under the natural development scenario, all land use types except cultivated land showed a decrease in area. Under the cultivated land protection scenario, cultivated land area reached 69 116.78 hm2, an increase of 8.23% compared to 2023, while forest land experienced the largest decrease. Under the urban development scenario, both cultivated land and construction land increased, whereas all other land types decreased. Under the ecological protection scenario, cultivated land, forest land, and grassland increased, while all other land types decreased. Under the sustainable development scenario, cultivated land area was slightly smaller than that under the cultivated land protection scenario, while forest land and grassland areas were similar to those under the ecological protection scenario. The spatial distribution characteristics of carbon storage were similar across all scenarios, consistently showing a pattern of lower values in the central area and higher values in the surrounding areas. Under the urban development scenario, total carbon storage was the lowest among all scenarios, decreasing by 3.01×105 t compared with the 2023 level. [Conclusions] This research reveals that construction land expansion is the main cause of carbon storage loss, while ecological restoration measures can effectively increase carbon storage. The spatial distribution of different land categories remains relatively stable across scenarios, with carbon storage exhibiting a pattern of lower values in the central urban area and higher values on the periphery. Strategies pursuing only economic development or focusing solely on cultivated land protection have inherent limitations. The sustainable development scenario can better balance cultivated land protection and ecological conservation, dynamically adjust the relationship between the two, and achieve their long-term coordinated development. This research could provide a reference for achieving carbon peak, carbon neutrality, and sustainable development goals in plain agricultural regions.

Key words: land use, carbon storage, PLUS-InVEST coupled model, multi-scenario simulation, spatiotemporal evolution, plain agricultural region

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